A novel data envelopment analysis model for solving supplier selection problems with undesirable outputs and lack of inputs

Mahdi Mahdiloo, Reza Farzipoor Saen, Madjid Tavana*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

19 Citations (SciVal)


Supplier evaluation and selection problems are inherently multi-criteria decision problems. Numerous analytical techniques ranging from simple weighted scoring to complex mathematical programming approaches have been proposed to solve these problems. Data Envelopment Analysis (DEA) has been used to evaluate suppliers' performance when there are multiple inputs and outputs in the supplier selection problem. The DEA determines the relative efficiencies of multiple suppliers. These relative efficiencies are then used to provide benchmarking data for reducing the number of suppliers. The DEA models used for supplier selection require numerical data for all the inputs and outputs for all the suppliers. However, this information may not be readily available in real-world problems. In this paper, we propose a novel DEA model that addresses this gap in the supplier evaluation literature. The proposed model can measure suppliers' efficiency in problems exhibiting: the presence of undesirable outputs; the lack of input variables and the presence of zero or negative values in the data set. We also present a case study at Saipa, Iran's second-largest car maker, to demonstrate the applicability of the proposed framework and exhibit the efficacy of the procedures and algorithms.

Original languageEnglish
Pages (from-to)285-305
Number of pages21
JournalInternational Journal of Logistics Systems and Management
Issue number3
Publication statusPublished - Mar 2012


  • Data envelopment analysis
  • DEA
  • Lack of inputs
  • Supplier selection
  • Translation invariance
  • Undesirable outputs

ASJC Scopus subject areas

  • Management Information Systems
  • Management Science and Operations Research
  • Information Systems and Management

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